人工智能和机器学习方法在设计癌症免疫疗法中的应用。

Q2 Medicine
Lokesh Seth, Colton Ladbury, Arya Amini
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引用次数: 0

摘要

人工智能(AI)和机器学习(ML)通过解决癌症与免疫系统之间复杂的相互作用,正在彻底改变癌症免疫治疗。本章探讨了人工智能技术如何在多个领域促进免疫治疗的发展:抗体设计、反应预测、生物标志物鉴定和t细胞靶点发现。在治疗性抗体设计中,AI通过抗体-抗原相互作用的预测建模、结构预测工具、创建新抗体序列的生成模型和可开发性优化来提高效率。临床应用包括使用多组学数据集成预测免疫治疗反应的人工智能系统,帮助区分假进展和真正的疾病进展。除了程序性细胞死亡蛋白1等传统生物标志物外,人工智能还可以识别其他标志物,包括肿瘤突变负担、微卫星不稳定性、免疫细胞浸润模式和新的基因组改变。多组学方法利用人工智能合成不同的数据类型,揭示复杂的生物标志物特征,更准确地预测治疗结果。对于t细胞靶标识别,下一代免疫编辑平台,如Gritstone的EDGE™系统,通过将测序技术与复杂的预测算法相结合,实现了人工智能驱动的方法,可以精确识别新抗原(表2.1)。这些平台支持个性化和共享抗原的免疫治疗方法,可能通过与先天免疫途径的整合而增强。尽管取得了显著进展,但在解决肿瘤异质性、免疫逃避机制和预测算法的技术限制方面仍然存在挑战。人工智能方法的不断完善,扩展到不同的癌症类型,并与互补的治疗方式相结合,代表了有希望的未来方向。总的来说,人工智能和机器学习将通过实现更精确、有效和个性化的治疗方法,利用免疫系统对抗癌症的能力,改变癌症免疫治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence and Machine Learning Approaches in Designing Immunotherapy in Cancer.

Artificial intelligence (AI) and machine learning (ML) are revolutionizing cancer immunotherapy by addressing the complex interplay between cancer and the immune system. This chapter explores how AI technologies enhance immunotherapy development across multiple domains: antibody design, response prediction, biomarker identification, and T-cell target discovery. In therapeutic antibody design, AI improves efficiency through predictive modeling of antibody-antigen interactions, structure prediction tools, generative models that create novel antibody sequences, and developability optimization. Clinical applications include AI-powered systems that predict immunotherapy responses using multi-omics data integration, helping distinguish pseudoprogression from true disease progression. Beyond conventional biomarkers like programmed cell death protein 1, AI enables identification of additional markers including tumor mutational burden, microsatellite instability, immune cell infiltration patterns, and novel genomic alterations. Multi-omics approaches leverage AI to synthesize diverse data types, uncovering complex biomarker signatures that more accurately predict treatment outcomes. For T-cell target identification, next-generation immunoediting platforms like Gritstone's EDGE™ system exemplify AI-powered approaches that precisely identify neoantigens by integrating sequencing technologies with sophisticated prediction algorithms (Table 2.1). These platforms support both personalized and shared antigen approaches to immunotherapy, potentially enhanced through integration with innate immune pathways. Despite remarkable progress, challenges persist in addressing tumor heterogeneity, immune evasion mechanisms, and technical limitations in prediction algorithms. The continued refinement of AI approaches, expansion to diverse cancer types, and integration with complementary therapeutic modalities represent promising future directions. Overall, AI and ML are poised to transform cancer immunotherapy by enabling more precise, effective, and personalized treatment approaches that harness the immune system's power against cancer.

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来源期刊
Cancer treatment and research
Cancer treatment and research Medicine-Oncology
CiteScore
1.00
自引率
0.00%
发文量
11
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